

from datetime import datetime as dt
import os

from github import Github


__snake_case				:      Dict   		=			[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						str 							= Github(os.environ["""GITHUB_TOKEN"""])
			a_						:						Any 							= g.get_repo("""huggingface/transformers""")
			a_						:						Optional[Any] 							= repo.get_issues(state="""open""")

			for issue in open_issues:
						a_						:						str 							= sorted([comment for comment in issue.get_comments()]  ,	key=lambda a__: i.created_at  ,	reverse=a_)
						a_						:						List[str] 							= comments[0] if len(a_) > 0 else None
						if (
						    last_comment is not None
						    and last_comment.user.login == "github-actions[bot]"
						    and (dt.utcnow() - issue.updated_at).days > 7
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
						):
									# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
									issue.edit(state="""closed""")
						elif (
						    (dt.utcnow() - issue.updated_at).days > 2_3
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
						):
									# print(f"Would add stale comment to {issue.number}")
									issue.create_comment(
									    """This issue has been automatically marked as stale because it has not had """
									    """recent activity. If you think this still needs to be addressed """
									    """please comment on this thread.\n\nPlease note that issues that do not follow the """
									    """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
									    """are likely to be ignored.""")


if __name__ == "__main__":
	main()









'''simple docstring'''






import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase									=   logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   *a_		,   a_=None		,   a_=None		,   a_=None		,   **a_						):
			super().__init__(*a_		,   **a_						)
			lowerCAmelCase  :     Tuple       						= eval_examples
			lowerCAmelCase  :     List[Any]       						= post_process_function
			lowerCAmelCase  :     List[Any]       						= quant_trainer_args
			lowerCAmelCase  :     Dict       						= 128  # default number of calibration samples



	def 	_lowerCamelCase     ( self		,   a_=None						):
			if calib_dataset is None and self.calib_dataset is None:
					raise ValueError("Trainer: calibration requires an calib_dataset."						)
			lowerCAmelCase  :     List[str]       						= calib_dataset if calib_dataset is not None else self.calib_dataset

			lowerCAmelCase  :     Union[str, Any]       						= self._remove_unused_columns(a_		,   description="Calibration"						)

			return DataLoader(
			    a_		,   batch_size=self.args.eval_batch_size		,   collate_fn=self.data_collator		,   drop_last=self.args.dataloader_drop_last		,   num_workers=self.args.dataloader_num_workers		,   pin_memory=self.args.dataloader_pin_memory		,   shuffle=a_		,   )



	def 	_lowerCamelCase     ( self		,   a_=None						):
			lowerCAmelCase  :     str       						= self.train_dataset if calib_dataset is None else calib_dataset
			lowerCAmelCase  :     str       						= self.get_calib_dataloader(a_						)

			lowerCAmelCase  :     Tuple       						= self.model
			quant_trainer.configure_model(a_		,   self.quant_trainer_args		,   calib=a_						)
			model.eval()
			quant_trainer.enable_calibration(a_						)

			logger.info("***** Running calibration *****"						)
			logger.info(F'''  Num examples = {self.calib_num}'''						)
			logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''						)

			for step, inputs in enumerate(a_						):
					# Prediction step
					lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= self.prediction_step(a_		,   a_		,   prediction_loss_only=a_						)
					if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

			quant_trainer.finish_calibration(a_		,   self.quant_trainer_args						)
			lowerCAmelCase  :     Union[str, Any]       						= model



	def 	_lowerCamelCase     ( self		,   a_=None		,   a_=None		,   a_=None		,   a_ = "eval"						):
			lowerCAmelCase  :     Optional[Any]       						= self.eval_dataset if eval_dataset is None else eval_dataset
			lowerCAmelCase  :     Optional[Any]       						= self.get_eval_dataloader(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= self.eval_examples if eval_examples is None else eval_examples

			# Temporarily disable metric computation, we will do it in the loop here.
			lowerCAmelCase  :     Dict       						= self.compute_metrics
			lowerCAmelCase  :     List[str]       						= None
			lowerCAmelCase  :     str       						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					lowerCAmelCase  :     Union[str, Any]       						= eval_loop(
					    a_		,   description="Evaluation"		,   prediction_loss_only=True if compute_metrics is None else None		,   ignore_keys=a_		,   )
			finally:
					lowerCAmelCase  :     List[str]       						= compute_metrics

			if self.post_process_function is not None and self.compute_metrics is not None:
					lowerCAmelCase  :     Optional[Any]       						= self.post_process_function(a_		,   a_		,   output.predictions						)
					lowerCAmelCase  :     Optional[Any]       						= self.compute_metrics(a_						)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()						):
							if not key.startswith(F'''{metric_key_prefix}_'''						):
									lowerCAmelCase  :     Any       						= metrics.pop(a_						)

					self.log(a_						)
			else:
					lowerCAmelCase  :     str       						= {}

			if self.args.tpu_metrics_debug or self.args.debug:
					# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
					xm.master_print(met.metrics_report()						)

			lowerCAmelCase  :     Dict       						= self.callback_handler.on_evaluate(self.args		,   self.state		,   self.control		,   a_						)
			return metrics



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_=None		,   a_ = "test"						):
			lowerCAmelCase  :     int       						= self.get_test_dataloader(a_						)

			# Temporarily disable metric computation, we will do it in the loop here.
			lowerCAmelCase  :     Union[str, Any]       						= self.compute_metrics
			lowerCAmelCase  :     List[str]       						= None
			lowerCAmelCase  :     Union[str, Any]       						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					lowerCAmelCase  :     int       						= eval_loop(
					    a_		,   description="Prediction"		,   prediction_loss_only=True if compute_metrics is None else None		,   ignore_keys=a_		,   )
			finally:
					lowerCAmelCase  :     str       						= compute_metrics

			if self.post_process_function is None or self.compute_metrics is None:
					return output

			lowerCAmelCase  :     List[str]       						= self.post_process_function(a_		,   a_		,   output.predictions		,   "predict"						)
			lowerCAmelCase  :     Any       						= self.compute_metrics(a_						)

			# Prefix all keys with metric_key_prefix + '_'
			for key in list(metrics.keys()						):
					if not key.startswith(F'''{metric_key_prefix}_'''						):
							lowerCAmelCase  :     Optional[Any]       						= metrics.pop(a_						)

			return PredictionOutput(predictions=predictions.predictions		,   label_ids=predictions.label_ids		,   metrics=a_						)





	def 	_lowerCamelCase     ( self		,   a_="./"						):
			lowerCAmelCase  :     Optional[Any]       						= self.eval_dataset
			lowerCAmelCase  :     Optional[int]       						= self.get_eval_dataloader(a_						)

			lowerCAmelCase  :     Union[str, Any]       						= next(iter(a_						)						)

			# saving device - to make it consistent
			lowerCAmelCase  :     Any       						= torch.device("cuda" if torch.cuda.is_available() else "cpu"						)

			# convert to tuple
			lowerCAmelCase  :     str       						= tuple(v.to(a_						) for k, v in batch.items()						)

			logger.info("Converting model to be onnx compatible"						)
			from pytorch_quantization.nn import TensorQuantizer

			lowerCAmelCase  :     Any       						= True

			lowerCAmelCase  :     Dict       						= self.model.to(a_						)

			model.eval()
			model.float()

			lowerCAmelCase  :     Union[str, Any]       						= model.module if hasattr(a_		,   "module"						) else model
			quant_trainer.configure_model(a_		,   self.quant_trainer_args						)

			lowerCAmelCase  :     Any       						= os.path.join(a_		,   "model.onnx"						)
			logger.info(F'''exporting model to {output_model_file}'''						)

			lowerCAmelCase  :     List[Any]       						= {0: "batch_size", 1: "seq_len"}

			torch.onnx.export(
			    a_		,   a_		,   a_		,   export_params=a_		,   opset_version=13		,   do_constant_folding=a_		,   input_names=["input_ids", "attention_mask", "token_type_ids"]		,   output_names=["output_start_logits", "output_end_logits"]		,   dynamic_axes={
			        "input_ids": axes,
			        "attention_mask": axes,
			        "token_type_ids": axes,
			        "output_start_logits": axes,
			        "output_end_logits": axes,
			    }		,   verbose=a_		,   )
			logger.info("onnx export finished"						)



